School of Mathematics, South China University of Technology, Guangzhou, 510640, China.
Department of Thoracic Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, China.
J Transl Med. 2022 Jun 6;20(1):254. doi: 10.1186/s12967-022-03445-0.
There are sudden deterioration phenomena during the progression of many complex diseases, including most cancers; that is, the biological system may go through a critical transition from one stable state (the normal state) to another (the disease state). It is of great importance to predict this critical transition or the so-called pre-disease state so that patients can receive appropriate and timely medical care. In practice, however, this critical transition is usually difficult to identify due to the high nonlinearity and complexity of biological systems.
In this study, we employed a model-free computational method, local network entropy (LNE), to identify the critical transition/pre-disease states of complex diseases. From a network perspective, this method effectively explores the key associations among biomolecules and captures their dynamic abnormalities.
Based on LNE, the pre-disease states of ten cancers were successfully detected. Two types of new prognostic biomarkers, optimistic LNE (O-LNE) and pessimistic LNE (P-LNE) biomarkers, were identified, enabling identification of the pre-disease state and evaluation of prognosis. In addition, LNE helps to find "dark genes" with nondifferential gene expression but differential LNE values.
The proposed method effectively identified the critical transition states of complex diseases at the single-sample level. Our study not only identified the critical transition states of ten cancers but also provides two types of new prognostic biomarkers, O-LNE and P-LNE biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.
许多复杂疾病（包括大多数癌症）的进展过程中都存在突然恶化的现象；也就是说，生物系统可能会经历从一个稳定状态（正常状态）到另一个状态（疾病状态）的关键转变。预测这种关键转变或所谓的疾病前期状态非常重要，这样患者就可以得到适当和及时的医疗护理。然而，在实践中，由于生物系统的高度非线性和复杂性，这种关键转变通常难以识别。
在这项研究中，我们采用了一种无模型的计算方法，即局部网络熵（LNE），来识别复杂疾病的关键转变/疾病前期状态。从网络的角度来看，这种方法有效地探索了生物分子之间的关键关联，并捕捉了它们的动态异常。
基于 LNE，成功地检测到了十种癌症的疾病前期状态。我们确定了两种新的预后生物标志物，乐观 LNE（O-LNE）和悲观 LNE（P-LNE）生物标志物，能够识别疾病前期状态和评估预后。此外，LNE 有助于发现具有非差异基因表达但差异 LNE 值的“暗基因”。
该方法能够有效地在单一样本水平上识别复杂疾病的关键转变状态。我们的研究不仅确定了十种癌症的关键转变状态，还为进一步的实际应用提供了两种新的预后生物标志物，即 O-LNE 和 P-LNE 生物标志物。因此，该研究中的方法在个性化疾病诊断方面具有很大的潜力。
